Learning-based Sorting

Learning-Based Sorting is a computational methodology within automated sorting systems that employs machine learning models to classify or order items by learning patterns and decision rules from data, distinguishing it from rule-based or heuristic sorting approaches through its reliance on adaptive, data-driven inference.

15

Publications

616

Citations

41

Authors

21

Institutions

Publications per year

2017–2026

5

Authors

41

  1. DE
    Daniel E. Guyer

    Michigan State University, United States

    2 publications · 151 citations

    2

    h-index

  2. İK
    İsmail Kavdır

    Çanakkale Onsekiz Mart Üniversitesi, Türkiye

    2 publications · 151 citations

    2

    h-index

  3. KT
    Kumiko Tanaka‐Ishii

    The University of Tokyo, Japan

    2 publications · 94 citations

    2

    h-index

  4. ST
    Satoshi Tezuka

    The University of Tokyo, Japan

    2 publications · 94 citations

    2

    h-index

  5. RD
    Robertas Damaševičius

    Kaunas University of Technology, Lithuania

    1 publications · 137 citations

    1

    h-index

1–5 of 41

Venues

  1. Biosystems Engineering

    2 publications · 151 citations

    2

    h-index

  2. Computational Linguistics

    2 publications · 94 citations

    2

    h-index

  3. Journal of Energy Storage

    1 publications · 22 citations

    1

    h-index

  4. Aberdeen University Research Archive (Aberdeen University)

    Repository · 1 publications · 24 citations

    1

    h-index

  5. International Conference on Machine Learning

    Conference · 1 publications · 20 citations

    1

    h-index

1–5